{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.66843342781067\n",
      "999.139930384655\n",
      "<class 'pandas.core.series.Series'>\n",
      "   id  node  batchsize      delay     tx          tps  send  round\n",
      "0   7    16       1000  19.752337  16000   810.030713  5590      0\n",
      "1   7    16       1000  15.745284  16000  1016.177314  4580      1\n",
      "2   7    16       1000  12.546549  16000  1275.251083  4180      2\n",
      "3   7    16       1000  14.308185  16000  1118.241055  4130      3\n",
      "4   7    16       1000  15.786919  16000  1013.497291  4540      4\n",
      "5   7    16       1000  14.673965  16000  1090.366494  4410      5\n",
      "6   7    16       1000  23.865794  16000   670.415563  6900      6\n"
     ]
    }
   ],
   "source": [
    "# 非分片tps处理\n",
    "import pandas as pd\n",
    "path = 'hbbft_16_1000-5000.csv'\n",
    "df = pd.read_csv(path)\n",
    "# print(df)\n",
    "# data = (df.loc[df['id'] == 7], df['node'] == 16, df['batchsize'] == 1000)\n",
    "# data = (df.loc[df['id'] == 7], df.loc[df['node'] == 16], df.loc[df['batchsize'] == 1000])\n",
    "flag =[]\n",
    "\n",
    "data = (df.loc[(df['id'] == 7) & (df['node'] ==16) & (df['batchsize'] == 1000),:])\n",
    "de = (data['delay'])\n",
    "mean_delay = (data['delay'].astype('float').sum()) / 7\n",
    "mean_tps = (data['tps'].astype('float').sum()) / 7\n",
    "print(mean_delay)\n",
    "print(mean_tps)\n",
    "print(type(de))\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "this is the node: 16 batchsize: 1000 mean_tps 999.344651271388\n",
      "16\n",
      "this is the node: 16 batchsize: 2000 mean_tps 1972.2289261514516\n",
      "16\n",
      "this is the node: 16 batchsize: 3000 mean_tps 2937.647887242133\n",
      "16\n",
      "this is the node: 16 batchsize: 4000 mean_tps 3832.705760132002\n",
      "16\n",
      "this is the node: 16 batchsize: 5000 mean_tps 4776.674911356351\n",
      "32\n",
      "this is the node: 32 batchsize: 1000 mean_tps 1077.4867214368444\n",
      "32\n",
      "this is the node: 32 batchsize: 2000 mean_tps 2129.178293193729\n",
      "32\n",
      "this is the node: 32 batchsize: 3000 mean_tps 3164.126124867609\n",
      "32\n",
      "this is the node: 32 batchsize: 4000 mean_tps 4186.363996991946\n",
      "32\n",
      "this is the node: 32 batchsize: 5000 mean_tps 5213.898644643069\n",
      "64\n",
      "this is the node: 64 batchsize: 1000 mean_tps 1048.2883494088662\n",
      "64\n",
      "this is the node: 64 batchsize: 2000 mean_tps 2107.331668991244\n",
      "64\n",
      "this is the node: 64 batchsize: 3000 mean_tps 3134.0254816578854\n",
      "64\n",
      "this is the node: 64 batchsize: 4000 mean_tps 4128.601912180843\n",
      "64\n",
      "this is the node: 64 batchsize: 5000 mean_tps 5063.6338539309145\n",
      "96\n",
      "this is the node: 96 batchsize: 1000 mean_tps 971.676344146649\n",
      "96\n",
      "this is the node: 96 batchsize: 2000 mean_tps 2013.060324477379\n",
      "96\n",
      "this is the node: 96 batchsize: 3000 mean_tps 3015.4585471658975\n",
      "96\n",
      "this is the node: 96 batchsize: 4000 mean_tps 3963.538218363874\n",
      "96\n",
      "this is the node: 96 batchsize: 5000 mean_tps 4907.209631921947\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "path = 'hbbft.csv'\n",
    "protocol_list = ['hbbft.csv', 'dumbo.csv']\n",
    "node_list = [16, 32, 64, 96]\n",
    "batch_list = [1000, 2000, 3000, 4000, 5000]\n",
    "df = pd.read_csv(path)\n",
    "total = 0\n",
    "\n",
    "mean = []\n",
    "r = 7\n",
    "node = 16\n",
    "batchsize = 1000\n",
    "for i in node_list:\n",
    "    for j in batch_list:\n",
    "        for h in range(i):\n",
    "            data = (df.loc[(df['id'] == h) & (df['node'] == i) & (df['batchsize'] == j),:])\n",
    "            mean_tps = (data['tps'].astype('float').sum()) / r\n",
    "            mean.append(mean_tps)\n",
    "        # print(mean)\n",
    "        print(i)\n",
    "\n",
    "        for n in range(i):\n",
    "            total += mean[n]\n",
    "        mean_mean = total / node\n",
    "        total = 0\n",
    "        mean.clear()\n",
    "        print('this is the node:',i,'batchsize:',j,'mean_tps', mean_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3572563/1724372778.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msend_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mtotal_send\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0msend_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mmean_delay\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtotal_delay\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelay_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0mmean_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mmean_send\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtotal_send\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msend_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "path = 'hbbft.csv'\n",
    "protocol_list = ['hbbft.csv', 'dumbo.csv']\n",
    "node_list = [16, 32, 64, 96]\n",
    "batch_list = [1000, 2000, 3000, 4000, 5000]\n",
    "df = pd.read_csv(path)\n",
    "total = 0\n",
    "\n",
    "mean = []\n",
    "delay_list = []\n",
    "send_list = []\n",
    "r = 7\n",
    "node = 96\n",
    "batchsize = 2000\n",
    "total_delay = 0\n",
    "total_send = 0\n",
    "for i in range(node):\n",
    "    data = (df.loc[(df['id'] == i) & (df['node'] == node) & (df['batchsize'] == batchsize * node),:])\n",
    "    delay = (data['delay'].astype('float').sum()) / r\n",
    "    mean_tps = (data['tps'].astype('float').sum()) / r\n",
    "    send = (data['send'].astype('float').sum()) / r\n",
    "    if mean_tps != 0 and delay != 0 and send != 0:\n",
    "        mean.append(mean_tps)\n",
    "        delay_list.append(delay)\n",
    "        if send > 200:\n",
    "            send_list.append(send)\n",
    "for n in range(len(mean)):\n",
    "    total += mean[n]\n",
    "for i in range(len(delay_list)):\n",
    "    total_delay += delay_list[i]\n",
    "for i in range(len(send_list)):\n",
    "    total_send += send_list[i]\n",
    "mean_delay = total_delay / len(delay_list)\n",
    "mean_mean = total / len(mean)\n",
    "mean_send = total_send / len(send_list)\n",
    "print('this is the node:',node,'batchsize:',batchsize,'mean_delay', mean_delay,'mean_tps', mean_mean,'mean_send:', mean_send)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'shard_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.conda/envs/hbb/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hbb/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hbb/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'shard_id'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1674233/413432833.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mttx\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mtx_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshard\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'shard_id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'node'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'batchsize'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mbatchsize\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'shard'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshard\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m     \u001b[0;31m# txa =(data['tx'].astype('float').sum()) / (shard*r)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;31m# print(txa, i)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hbb/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/hbb/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'shard_id'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "path = 'hbbft.csv'\n",
    "df = pd.read_csv(path)\n",
    "shard = 4\n",
    "batchsize = 5000\n",
    "node = 16\n",
    "r = 7\n",
    "shard_amount = node / shard\n",
    "mean_shard = []\n",
    "tx_list = []\n",
    "send_list = []\n",
    "total_delay = 0\n",
    "total_tx = 0\n",
    "total_send = 0\n",
    "ttx = 0\n",
    "for i in range(node):\n",
    "    data = (df.loc[(df['id'] == i) & (df['node'] == node) & (df['batchsize'] == batchsize),:])\n",
    "    delay = (data['delay'].astype('float').sum()) / r\n",
    "    tx =(data['tx'].astype('float').sum()) / r\n",
    "    send = (data['send'].astype('float').sum()) / r\n",
    "    if tx != 0:\n",
    "        # print('this is the node tx', i, tx)\n",
    "        tx_list.append(tx)\n",
    "    if send > 200:\n",
    "        send_list.append(send)\n",
    "    if delay != 0:\n",
    "        mean_shard.append(delay)\n",
    "for i in range(len(mean_shard)):\n",
    "    total_delay += mean_shard[i]\n",
    "mean_delay = total_delay / len(mean_shard)\n",
    "for i in range(len(send_list)):\n",
    "    total_send += send_list[i]\n",
    "for i in range(len(tx_list)):\n",
    "    # print(total_tx, tx_list[i])\n",
    "    ttx += tx_list[i]\n",
    "for i in range(shard):\n",
    "    data = (df.loc[(df['shard_id'] == i) & (df['node'] == node) & (df['batchsize'] == batchsize) & (df['shard'] == shard),:])\n",
    "    # txa =(data['tx'].astype('float').sum()) / (shard*r)\n",
    "    # print(txa, i)\n",
    "total_tx = (ttx/len(tx_list)) * shard\n",
    "mean_tps = (total_tx / mean_delay)\n",
    "# mean_tx = total_tx / len(tx_list)\n",
    "mean_tx = total_tx / shard\n",
    "mean_send = total_send / len(send_list)\n",
    "\n",
    "print('node:',node,'batchsize:',batchsize,'shard_number:', shard,'mean_delay:', mean_delay,'total tx:', total_tx,'mean_tx:',mean_tx, 'mean_tps:', mean_tps,'mean_send:', mean_send)\n",
    "# print('this is the node:', node, 'batchsize:', batchsize,'mean_tps :', mean_tps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node: 96 batchsize: 5000 mean_delay: 68.59302657452368 total tx: 360000.0 mean_tx: 360000.0 mean_tps: 5248.34692355897 mean_send: 965.2017857142858\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "path = 'dumbo.csv'\n",
    "df = pd.read_csv(path)\n",
    "shard = 4\n",
    "batchsize = 5000\n",
    "node = 96\n",
    "r = 7\n",
    "shard_amount = node / shard\n",
    "mean_shard = []\n",
    "tx_list = []\n",
    "send_list = []\n",
    "total_delay = 0\n",
    "total_tx = 0\n",
    "total_send = 0\n",
    "ttx = 0\n",
    "for i in range(node):\n",
    "    data = (df.loc[(df['id'] == i) & (df['node'] == node) & (df['batchsize'] == batchsize * node),:])\n",
    "    delay = (data['delay'].astype('float').sum()) / r\n",
    "    tx =(data['tx'].astype('float').sum()) / r\n",
    "    send = (data['send'].astype('float').sum()) / r\n",
    "    if tx != 0:\n",
    "        # print('this is the node tx', i, tx)\n",
    "        tx_list.append(tx)\n",
    "    if send > 200:\n",
    "        send_list.append(send)\n",
    "    if delay != 0:\n",
    "        mean_shard.append(delay)\n",
    "for i in range(len(mean_shard)):\n",
    "    total_delay += mean_shard[i]\n",
    "mean_delay = total_delay / len(mean_shard)\n",
    "for i in range(len(send_list)):\n",
    "    total_send += send_list[i]\n",
    "for i in range(len(tx_list)):\n",
    "    # print(total_tx, tx_list[i])\n",
    "    ttx += tx_list[i]\n",
    "\n",
    "total_tx = ttx/len(tx_list)\n",
    "mean_tps = (total_tx / mean_delay)\n",
    "mean_tx = total_tx / len(tx_list)\n",
    "mean_send = total_send / len(send_list)\n",
    "\n",
    "print('node:',node,'batchsize:',batchsize,'mean_delay:', mean_delay,'total tx:', total_tx,'mean_tx:',total_tx, 'mean_tps:', mean_tps,'mean_send:', mean_send)\n",
    "# print('this is the node:', node, 'batchsize:', batchsize,'mean_tps :', mean_tps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "consus = [\n",
    "    [999.344651271388,13551.868286753004,9684.476891828554],\n",
    "    [1972.2289261514516,23614.632791418135,17784.056132237685],\n",
    "    [2937.647887242133,30785.888498003704,23974.74051790485],\n",
    "    [3832.705760132002,36358.1397573404,32198.876029511746],\n",
    "    [4776.674911356351,40453.59206446519,36039.503565574145]\n",
    "]\n",
    "data_value = np.asarray(consus)\n",
    "batch_list = [1000, 2000, 3000, 4000, 5000]\n",
    "batch = np.asarray(batch_list)\n",
    "data = pd.DataFrame(data_value, batch, columns=[\"hbbft\", \"dumbo\", \"hbbft_shard\"])\n",
    "\n",
    "\n",
    "# data = data.rolling(7).mean()\n",
    "\n",
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "consus = [\n",
    "    [538.7433607184222,8756.585293305574,9194.306617889602],\n",
    "    [1064.5891465968646,14995.414256313847,16735.92540061401],\n",
    "    [1582.0630624338046,21002.571350271493,23763.256870229605],\n",
    "    [2093.181998495973,25163.438058597934,29291.45895795729],\n",
    "    [2606.9493223215345,28925.00794828205,35104.22712575278]\n",
    "]\n",
    "data_value = np.asarray(consus)\n",
    "batch_list = [1000, 2000, 3000, 4000, 5000]\n",
    "batch = np.asarray(batch_list)\n",
    "data = pd.DataFrame(data_value, batch, columns=[\"hbbft\", \"dumbo\", \"hbbft_shard\"])\n",
    "\n",
    "\n",
    "# data = data.rolling(7).mean()\n",
    "\n",
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "consus = [\n",
    "    [262.07208735221656,3405.7479932864185,5529.6797708578015],\n",
    "    [526.832917247811,5028.2487677134095,10726.463260931934],\n",
    "    [783.5063704144713,5958.233312359928,15428.208793855518],\n",
    "    [1032.1504780452108,6557.326447610786,19738.727499187913],\n",
    "    [1265.9084634827286,6956.06503566422,23500.321942491217]\n",
    "]\n",
    "data_value = np.asarray(consus)\n",
    "batch_list = [1000, 2000, 3000, 4000, 5000]\n",
    "batch = np.asarray(batch_list)\n",
    "data = pd.DataFrame(data_value, batch, columns=[\"hbbft\", \"dumbo\", \"hbbft_shard\"])\n",
    "\n",
    "\n",
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "consus = [\n",
    "    [161.94605735777483,2015.102170362338,2362.1975920985287],\n",
    "    [335.5100540795632,3293.6700914731628, 4531.884474264948],\n",
    "    [502.5764245276496,4158.43915566455,6494.427450378067],\n",
    "    [660.5897030606457,4778.928788668063,8283.547316745031], \n",
    "    [817.8682719869912,5248.546889603578,9885.056926186153]\n",
    "]\n",
    "data_value = np.asarray(consus)\n",
    "batch_list = [1000, 2000, 3000, 4000, 5000]\n",
    "batch = np.asarray(batch_list)\n",
    "data = pd.DataFrame(data_value, batch, columns=[\"hbbft\", \"dumbo\", \"hbbft_shard\"])\n",
    "\n",
    "\n",
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RokW4cuUKlixZUumYiYgI0BlMGLf+MA6dvyG2+WvU+HJ0Z/hrnG0YWe3BgqfIvEa37x/+ORD+oPnrHW8B+5aWv22jKOC5v8xf510H3g0ud1MFALx62YpAi61YsQIdOnTA/PnzAQDdu3dHYWEhPvroo0od77777sOECRMAAG3atMH27duxefNmbN++HUqleSj04MGD2Lp1q0XBM3ToUIwbN06MIyUlBcuXLxcLnk8++QRt27bF+++/DwCIiYmBi4sLZs2ahdOnT0tGnIiIqOIMRhMmfXsEu8+ki231XJVYPzoaTX1cbRhZ7cIpLTtlNBoRFxeHvn37Str79+9f6WN27dpV/NrDwwPe3t7o0KGDWOwAQPPmzXHlyhWLfUvH0a9fP8TFxcFoNCI3NxenT59Gv379JNsMHDgQAHD48OFKx0xEVJeZTAJe//E4thxPFdvc1U5Y+2wntPT3sGFktQ9HeIpMv8Moi0Jd/HWvWcC9r5e/raxEHenqc9tjG41GwFRud7kyMjJgMBjg7e0taa9fv771B7vFw0P6w6FSqaDRaCRtSqWyzDU8Pj4+FnHo9XrcuHEDhYWFEATBYhsPDw+oVCpkZmZWOmYiorpKEATM3RyPDYcvim1qJzlWP9UBbQK8bBdYLcWCp4jKreLbOqkAVHABmEx2+2MbjUBBQcWf+xZvb284OTlZXP/m2rVrkscqlQp6vXS9UVZWFqra9evX4e/vL4lDqVSiXr16yM3NhUwms4g1OzsbOp0Onp6eVR4PEZGj+/DPs/h0b5L42Ekuw4on26FzkM9t9qq7OKVlpxQKBcLCwrB9+3ZJ+++//y553KBBA5w7d07Stm/fviqPp3Qc27ZtQ3h4OBQKBdzc3KDVarFt2zbJNr/99hsAoH379lUeDxGRI1uzJwmL/zgrPpbJgEWPRqJXK//b7FW3cYTHjo0fPx4vvPACXn/9dfEsrY0bN0q26d+/P9auXYuIiAgEBgbil19+QVpaWpXHsnHjRjg7OyMsLAxbtmzBP//8g5UrV4r948aNwyuvvIIpU6ZgyJAhSEpKwqJFi9C/f38uWCYissJ3/6Tg7U0nJW3zHozA/W3vcPJNHccRHjvWu3dvzJkzB/v378eLL76IvXv3YvHixZJtXnjhBQwePBjLly/Hq6++ikaNGmHUqFFVHsv777+PPXv24MUXX8SBAwfw9ttvo0ePHmJ/jx49sGjRIpw5cwYvvPACVq1ahUceeQTvvvtulcdCROSofj95FdN+PCZpmz6wFR7r1NRGEdkPmSAIgq2DqCrHjx8HAERERJTZX1BQgKSkJAQGBsLZuXZcl8BoNKKgoADOzs5QKBzzkt+OlmPp91FeXh7i4+MRGhoKV1fHOwXU0fMDHD9H5mf/8vLy8NXO/7Bg700YTMUf2xN7tcD/+tn/KPmxY8cgk8nK/fyuCpzSIiIiquUOX7iJhftuwFDirN6nujTDK31DbBeUneGUFhERUS124lImnv/6GHTG4raH2jXGm/eHQyaT2S4wO8OCh4iIqJZKuJqDUZ8eRE5hcbXTL8wfCx9uA7mcxY41WPAQERHVQikZeXhydSwycosv9tolsB6WPh4FJwU/vq3F7xgREVEtczW7ACPXxCI1q/jCtFofJZY+GgG1k/2f/GELXLRMRERUi9zM02Hk6oNIvp4ntmn93TG9sytcVSx2KosjPERERLVETqEBT3/2D06nZYttgfXdsOqJtnBX8SP7bvC7R0REVAsU6I14bt0h/JdyU2xr5OmM9WOiUd+9gvdvpHKx4CEiIrIxvdGECV8dwb5z18U2HzcVvhgTjcZeLjaMzHGw4CEiIrIhk0nAqxuO4o/44vscejg7Yd3oTgj2dbdhZI6FBY+dmjZtGgYPHnzbbbRaLdasWXPbbWJjY6HVasXbctzOr7/+in79+iE8PBxDhw5FfHw8li5divz8fKtiv5ORI0di3LhxVXrMisjKyoJWq8WPP/5Y489NRHWTIAh485c4/PzfZbHNRanA5890RHgjTxtG5nhY8FCF5ObmYvr06Wjfvj3WrVuHhQsXIj4+HsuWLavygoeIqK549/fT+OLAefGxSiHHylHt0b6Ztw2jckwseKhCLl26BJ1OhyFDhqB9+/bQau3zZnVGoxF6vd7WYRARYcVf5/DRX+fEx3IZsOSxSHRv6WvDqByXVQXPjz/+CK1Wa/Hvvffek2y3YcMG9O/fHxERERgyZAh27txpcazs7GxMnz4dnTp1QlRUFF566SVcvXr17rKpg2JjY/HAAw8gMjISw4YNw4kTJyT9RqMRCxcuROfOnREVFYVp06YhJyfH4jgZGRmYMGECIiMj0a1bN3z88cdi39KlS3H//fcDAJ5++mlotVr06tULr7/+OgCgS5cuYltFHD58GE888QTat2+PqKgo3H///fjpp58sttu6dSv69++PqKgojBo1ChcuXJD0v/fee7j//vsRFRWF7t2745VXXrF4DxVNj/3000/ie/LUqVMAgO+++w69evVC27Zt8dRTT+H8+fMgIqoJ6w+cxztbT0naFg5ri/taN7RRRI6vUhceXL16NTw8PMTH/v7+4tebN2/GzJkzMX78eHTu3BlbtmzBhAkT8OWXXyIyMlLcbtKkSUhISMDs2bOhVquxePFijB07Fj/88AOcnGr+eoh5evMFnlycXCCTyZBvyIcgCFAr1FDIFSg0FsJoMkKpUEIpV0Jv1ENv0sNJ7gSVQgWDyQCdUQe5TA5nJ2eYBBMKDOYrZLoqXct9DqPRCBkqdz+U9PR0zJ07F8899xw8PDzw/vvvY8KECdi+fTuUSiUA4IsvvkB4eDjeeecdXLx4Ee+99x4KCwuxaNEiybFmzpyJQYMGYenSpdi3bx8WLVoET09PPPbYYxg+fDiaNGmCqVOnYtasWQgPD4e/vz++/fZbrFixQnw/qFR3Pm0yJycH48aNQ/v27fHBBx9ApVIhISEBWVlZku3i4+ORkZGBKVOmwGg0YsGCBXj11Vfx7bffittcv34d48aNg5+fHzIyMvDZZ59h5MiR2Lx5s+Q9dOLECVy6dAkvv/wyNBoNGjZsiJ07d2LmzJl46KGHMHDgQMTFxeHll1+u1OtARGSNjf9dwsyN0j9O37w/DMPaB9goorqhUpVFeHg4vL3Lnl9csmQJBg0ahEmTJgEAOnfujDNnzmD58uVYtWoVAODIkSPYs2cP1qxZg27dugEAAgMDMXDgQGzbtg0DBw6sTFh3JfqraADArkd3wdvZG49tegznMs/h0/6fomODjnj979ex/fx2TI+ejsdaPYZVx1dhxdEVeFT7KGZ0noE/L/yJKbumoIN/B3x232dIvJmIB395EPXU9bB7xO7bPseBYQcqFXNmZibWr1+Pli1bAgBcXFwwatQoHD16FB06dAAAqFQqLF++HAqF+eqcarUaM2bMwIQJExAcHCweq3Pnzpg6dSoAoHv37rh+/TpWrFiBRx99FA0aNBCnsFq0aCEWrk2bNgVw+/dDaUlJScjOzsYrr7wiHrNLly4W22VnZ+Pnn38Wj5uXl4fXX38dqampaNCgAQBg/vz54vZGoxFRUVGIiYnBgQMHxPdV0ffp+++/R8OGxX85rVixAh06dBCP0b17dxQWFuKjjz6qUB5ERJXxx8k0vPLdUQhCcdsrfUPwTNdA2wVVR1TpGp6UlBQkJydjwIABkvaBAwdi//790OnMN0DbvXs3NBoNunbtKm4TFBSE0NBQ7N69uypDcmh+fn5isQOYixEASEsrPrWxZ8+eYrEDAPfddx8EQbA4K6tv376Sx/3790daWhpSU1OrNOamTZvC3d0ds2fPxpYtW5CRkVHmdq1atZIUUUW5lYxn165dGDFiBNq3b4+wsDDExMQAAJKTkyXHCgkJkRQ7RqMRcXFxZeZMRFRd9p27hhe++hdGU3G1M7Z7ICb2amHDqOqOSo3wDB48GDdu3ECjRo3wyCOPYMyYMVAoFEhMTARgHq0pKTg4GHq9HikpKQgODkZiYiICAwMhk0mncoKCgsRjVJYgCMjLyyuzr7CwECaTCUajEUajUdK3f8R+AICzwhlGoxHrB6wHAKjkKhiNRsy9Zy7e6vIWnOROMBqNeDbsWYwKHQWFTAGj0Yh7G9+L/SP2QwYZjEYjmnk0E49Z9FxlPYcgCIDRHHfpmO6Up4eHh2Qfudxcv+bn54vt9erVk2zj4uICtVqNtLQ0GI1GmEwmAICXl5dku3r16gEwFxj+/v5iX9H3r+jr0m3lxVr0f3d3d6xevRrLli3Da6+9BqPRiPbt2+ONN95ASEhIhXM7fvw4XnjhBfTq1QujR4+Gj48PAOCxxx6T5C8IAnx8fCTHSk9Ph8FgKDfnO+VT9H3Lz88X/18UmyNy9PwAx8+R+dnesUtZGPPFf9AZTGLbsKiGmHRvswrFbQ853g1BECxqgqpmVcHj6+uLiRMnom3btpDJZNixYwcWL16MtLQ0zJo1C5mZmQAAjUYj2a/ocVF/VlaWZA1QEU9PT4tFt9bS6/WIj48vt9/JyQmFhYUW7UXraAoNhZLH+lv/FbUZb/1X9NgEEwr0BZJ9itbulPe45HOIbWXEdDtGoxGCIKCgoPhOukXH0Ov1Ynt6erpkm5ycHBQWFsLLywsFBQXiqFtqaqpku6KRFI1GI9lOp9OJ2xWd7VRQUCDZtzxF8YWEhGDJkiUoKCjAoUOHsGjRIkyYMAG//PILgOKCo+QxSz//1q1b4e7ujnnz5onF0OXL5utYGAwGcV+TyQSTySQ5lqurK5ycnJCWliZpL9q/5PevvDwMBoNFcV56ZMnROHp+gOPnyPxs40KmHjN3ZiBPXzyyc0+AMx4JNoknUVRUbc2xKlRkHejdsKrg6d69O7p37y4+7tatG9RqNdauXYvx48dXeXCVoVQqxemP0goLC3H58mWo1Wo4OzvXcGRlEwQBhYWFUKvVVlW3CoUCMplMkkdRUaBUKsX2v//+G9OnTxentTZv3gyZTIaoqCg4OzuLb7Ddu3dL1k7t3LkTfn5+aNasGeRyubidSqUSj+3qal6MXTqOiubo7OyMPn36IDU1FfPnz4dMJoNarYZcLodCoZAcs/TzG41GODk5wcXFRTzm9u3bAZiL2qJ9yzoWAISGhmLXrl0YM2aM2LZr1y6L7195nJyc0LRpU6jVauTn5yM5ORnNmzeHi4vjXQLe0fMDHD9H5mc7FzLy8X+//YucEsVO9xbeWPpoBFSKiq8qqc05VoWzZ89W+3Pc9elQAwYMwKeffor4+Hh4epqvCpmdnQ1f3+LrCBSdgVPUr9FoylwbkpmZKW5TWTKZTPwgLk0ul4sfgCXXtdhS0dSJTCazKiaZTGaxT9HXRTkC5iLopZdewmOPPSaepdW/f39x+qhodCQ2Nhbvvfceunbtir179+KXX37BrFmzxLO9yjp20fqhb775Bn369IGzs3OZ1+cpmePff/+N77//Hn369EGjRo1w7do1fPXVV2jXrp2kgLpTbt26dcO6deswb9489O3bF0eOHMHGjRstYizrWADw/PPP44UXXsCMGTPEs7SKRphK7l8WhUIBuVwOFxcXSWHk4uJS7nvPETh6foDj58j8ataVzHyM+fIoruXoxLZOzb2xclQnuKgq9xlU23KsKtU9nQVUQcFTUlBQEAAgMTFR/LrosVKpRJMmTcTt9u/fbzFnl5SUJH4QU9UYOXIkMjIy8Nprr0Gn06Fv376YNWuWxXZvvfUWvv32W3z99ddwc3PDyy+/jCeeeOK2xw4LC8PEiROxYcMGrF69Gg0bNsSOHTtuu0/Tpk0hl8uxePFiXL9+HV5eXujWrRteeeUVq/Lq0aMHpkyZgvXr1+PHH39Eu3bt8Mknn1R44XHv3r0xZ84cfPzxx9i8eTPatm2LxYsXY/jw4VbFQURUlus5hXhydSwu3ihecxPR2BNrnu5Q6WKH7o5MEEqeHGe9BQsWYN26ddi1axd8fX3Fi7uVvBjhY489Bnd3d8lp6SNGjMBnn32Ge+65B4C52BkwYAA++OCDSp+WXnTmUURERJn9BQUFSEpKQmBgYK2Z0ipaq+Ls7FxrRp2qmqPlWPp9lJeXh/j4eISGhjrkX16Onh/g+Dkyv5qVVaDH46sO4MSl4uuLtfBzx3fjusDbrXLrVGpbjlXt2LFjkMlk5X5+VwWrRnhGjx6N6Ohocdrizz//xHfffYdRo0aJU1gTJ07ElClT0LRpU0RHR2PLli04duwY1q9fLx4nKioK3bp1w/Tp0zF16lSo1WosWrQIWq0W/fr1q8L0iIiIak6+zogxnx+SFDsB9VywfnR0pYsdqhpWFTyBgYH44YcfkJqaCpPJhObNm2P69OkYOXKkuM3gwYORn5+PVatWYeXKlQgMDMSyZcsQFRUlOdbixYsxf/58zJo1CwaDAd26dcOMGTNscpVlqjpFZ4+VbjMYDDAYDA4xwkNEVBadwYTnvzyMg8nF1xfz81DjyzHRaOBZO2YV6jKrqosZM2ZUaLvhw4ffcS2Eh4cH5s2bh3nz5lkTAtVyffv2xaVLl8rtP336dA1GQ0RUM4wmAZO//Q9/nU4X27xclfhidDSa+bjZMDIqwuEUqlIrVqwQT48vYjKZoNPpqv0aC0REtiAIAqb/eBybj18R29xUCnz+TCdoG1hec45sgwUPVanyTksvWrRMRORIBEHA/22Ox7eHUsQ2lZMcq5/qiMgmXrYLjCxU6b207MVdnphGdRzfP0RUZOmOBKzekyQ+dpLLsOKJdugS7GPDqKgsdargKVoQbTAYbBwJ2bOi9w8X2BPVbZ/tTcIH28+Ij2Uy4P1H2qJ3qL8No6Ly1KmCp+gKy0VXfiaqjKysrFp1tW4iqnkbDqVgzq8nJW1zH2iNoZGNbRQR3Umd+hNVJpPBz88PV65cgVqthpubW41czvp2jEajeFNNR/0AdZQcBUFAbm4usrKy0LBhQ5u/d4jINraeuIKpPxyTtE0b0ApPRDezUURUEXWq4AHM9/PKz8/HtWvXkJ6efucdqpnJZILBYICTk5N4XytH40g5ymQyeHl53fU934jIPu0+k46JXx+BqcRSvhfuDcb4HsG2C4oqpM4VPDKZDA0bNoSfnx/0er2tw0F+fj4SExPRtGlTh7wDLuBYOSqVSrsepSKiyjuUnIHnvjgEvbG42hnZuRle7W95dirVPnWu4ClSW9ZgmEwmAIBarXbY07brQo5E5NjiLmfimc//QYHeJLY9GNUYc4aEc3rbTtj3/AIREVE1O5eeg1FrDiK7oPgM375h/nh3WBvI5Sx27AULHiIionJcupmPkatjcT23+AryXVv4YOljUXBS8CPUnvDVIiIiKkN6diGeXB2Ly5kFYltkEy+sHNkBzkrbL4kg67DgISIiKiUzT4+Ra2KRdC1XbGvVwAOfP9MRbuo6u/zVrrHgISIiKiG30ICnPz+IU6nZYltzH1esG90JXq68CbK9YsFDRER0S4HeiOe+OIQjF26KbQ09nbF+TDT8PHiWqT1jwUNERATAYDThpa+PYG/CdbHNx02FL0ZHI6Ceqw0jo6rAgoeIiOo8k0nAa98fw7aTaWKbh9oJa5/thBZ+7jaMjKoKCx4iIqrTBEHAnF/j8OORS2Kbs1KOT5/piNaNeRsZR8GCh4iI6rT3t53B2v3nxcdKhQyfjOyAjs29bRgVVTUWPEREVGd9susclu1MEB/LZcCHI6LQI8TXhlFRdWDBQ0REddLXBy9g/m+nJG0LHm6DgRENbRQRVScWPEREVOf8cvQypv90XNI2a3AYHunQxEYRUXVjwUNERHXKjlNpeOXb/yAIxW2T+rTEs90CbRcUVTsWPEREVGccSLyO59f/C4OpuNoZ3S0QL/duacOoqCaw4CEiojrhaMpNjP78HxQaTGLbIx0CMGNQKGQymQ0jo5rAgoeIiBzembRsPPXZQeTqjGLbwIgGmP9QGxY7dQQLHiIicmgXrufhydWxuJmnF9t6hPhi8aNRUMhZ7NQVLHiIiMhhpWUV4Ik1B3A1u1Bs69i8Hj5+sj1UTvwIrEv4ahMRkUPKyNXhydWxSMnIF9vCG2mw5umOcFEpbBgZ2QILHiIicjjZBXo8/dlBnL2aI7YF+7ph3bOdoHFW2jAyshUWPERE5FAK9EaMXnsIxy5mim2NvVywfkw0fNzVNoyMbIkFDxEROQydwYTn1x/GwaQMsa2+uxpfjolGQ08XG0ZGtsaCh4iIHILRJOCV7/7DztPpYpunixLrx3RC8/puNoyMagMWPEREZPcEQcBbW05j07ErYpurSoHPn+mIVg00NoyMagsnWwdARER0NwRBwLpj2fjlTJ7YpnKSY/WoDohqWs+GkVFtwhEeIiKya5/sOS8pdhRyGZY/3g73tKhvw6iotmHBQ0REdmvtvmQs2ZkkPpbJgPeHt0XfMH8bRkW1EQseIiKySz8cvog3f4mTtL01tDUeiGpso4ioNmPBQ0REdmfriVS8+v1RSdvkXkEY2bmZjSKi2o4FDxER2ZU9Z6/hpa+PwCQUtz2gdcPYbix2qHw8S4uIiOzG4fM3MHbdIeiMJrHt0faNMDzQaMOoyB5whIeIiOzCyctZeOazg8jXFxc3QyMbYebAEMhkMhtGRvaABQ8REdV6iek5GPVpLLIKDGJbn1A/vDe8LeQsdqgCWPAQEVGtdulmPp5cHYtrOTqxrUuQD5Y93g5KBT/GqGLu6p2Sm5uLmJgYaLVaHD9+XNK3YcMG9O/fHxERERgyZAh27txpsX92djamT5+OTp06ISoqCi+99BKuXr16NyEREZEDuZZTiJGrY3E5s0Bsa9vEC6ue6gBnpcKGkZG9uauC56OPPoLRaLlQbPPmzZg5cyYGDBiAVatWITIyEhMmTMB///0n2W7SpEnYu3cvZs+ejffeew9JSUkYO3YsDAaDxTGJiKhuyczXY9Sag0i8liu2af09sPaZjnBX85wbsk6lC55z587hq6++wsSJEy36lixZgkGDBmHSpEno3Lkz3nrrLURERGD58uXiNkeOHMGePXvwf//3fxg4cCB69+6NDz/8EKdPn8a2bdsqGxYRETmAPJ0Bz37+D05eyRLbmvm44ovRneDlqrJhZGSvKl3wzJ07FyNGjEBgYKCkPSUlBcnJyRgwYICkfeDAgdi/fz90OvMc7O7du6HRaNC1a1dxm6CgIISGhmL37t2VDYuIiOxcocGIcV8cxuHzN8S2BhpnrB8dDT+Nsw0jI3tWqTHBrVu34syZM1i6dCni4qSX9U5MTAQAi0IoODgYer0eKSkpCA4ORmJiIgIDAy1OJQwKChKPURmCICAvL+/OG9YS+fn5kv87IkfPkfnZP0fP0Z7yM5hM+N/3J/H32WtiWz1XJVY90QY+zijz97s95VdZjp6jIAjVfmkBqwue/Px8LFiwAJMnT4a7u7tFf2ZmJgBAo9FI2oseF/VnZWXBw8PDYn9PT0+cOHHC2rBEer0e8fHxld7fVpKTk20dQrVz9ByZn/1z9Bxre34mQcDyfzLx1/niBcquTjJMv0cD3bULiL92m51R+/OrCo6co0pVvVOVVhc8K1asgI+PDx5++OHqiOeuKZVKtGjRwtZhVFh+fj6Sk5PRvHlzuLi42DqcauHoOTI/++foOdpDfoIgYN7vZyXFjtpJjo+faIsOzbxuu6895He3HD3Hs2fPVvtzWFXwXLp0CZ9++imWL1+O7OxsAMXDi3l5ecjNzYWnpycA8ynnvr6+4r5ZWeaFZ0X9Go0GqampFs+RmZkpblMZMpkMrq6uld7fVlxcXOwybms4eo7Mz/45eo61Ob8Ptp/BlwcviY+VChk+HtkeMVq/Ch+jNudXVRw1x5q4UrZVBc/Fixeh1+vx3HPPWfSNGjUKbdu2xfvvvw/AvJYnKChI7E9MTIRSqUSTJk0AmNfq7N+/32LeLikpCSEhIZVKhoiI7M/qvxOx5M/iv/DlMmDxo1HoaUWxQ3QnVhU8oaGhWLdunaQtPj4e8+fPx5w5cxAREYEmTZqgefPm2Lp1K/r06SNut2XLFnTp0kWco4uJicFHH32E/fv345577gFgLnZOnjyJMWPG3G1eRERkB745eAFzN0vXXc5/KAKD2jS0UUTkqKwqeDQaDaKjo8vsCw8PR3h4OABg4sSJmDJlCpo2bYro6Ghs2bIFx44dw/r168Xto6Ki0K1bN0yfPh1Tp06FWq3GokWLoNVq0a9fv7tIiYiI7MGmY5fx+k/Sq/TPGBSKRzs2tVFE5Miq5VKVgwcPRn5+PlatWoWVK1ciMDAQy5YtQ1RUlGS7xYsXY/78+Zg1axYMBgO6deuGGTNmwMmJV9AkInJkO09dxaRv/oMgFLe91LslxnQPKn8nortw15VFdHQ0Tp8+bdE+fPhwDB8+/Lb7enh4YN68eZg3b97dhkFERHYiNvE6xq8/DIOpuNp5pmtzTO7T0oZRkaPjbWaJiKjGHL+YidFrD6HQYBLbhrUPwMxBYTVypg7VXSx4iIioRpxNy8aoT2ORU1h8g+gBrRtgwUMRkMtZ7FD1YsFDRETVLiUjD0+uicWNPL3Y1r1lfSweEQknBT+KqPrxXUZERNXqalYBnlgdi7SsQrGtQ7N6+GRke6idFDaMjOoSFjxERFRtbuTq8OSaWFzIKL7pZ1hDDdY83RGuKp6RSzWHBQ8REVWLnEIDnv7sIM6k5YhtQfXdsG50J3i6KG0YGdVFLHiIiKjKFeiNGLP2Hxy9mCm2NfZywfox0ajvrrZhZFRXseAhIqIqpTeaMOGrf3EgMUNsq++uxvox0Wjk5Xh3+ib7wIKHiIiqjNEk4H/fHcUf8VfFNo2zE74Y3QmB9d1sGBnVdSx4iIioSgiCgFkbT+CXo5fFNleVAp8/2wmhDTU2jIyIBQ8REVWRd7aexpexF8THKoUcK0d2QLum9WwYFZEZCx4iIrprH/2VgI93nRMfK+QyLH08Ct1a1rdhVETFWPAQEdFd+WJ/MhZuld5E+t1hbdA/vIGNIiKyxIKHiIgq7acjFzFzY5yk7a2h4XioXYCNIiIqGwseIiKqlG1xqZiy4Zik7dX+Wozq0tw2ARHdBgseIiKy2t6Ea5jw1REYTYLYNi4mCC/cG2zDqIjKx4KHiIis8u+FGxi77hB0RpPY9linppg2oBVkMpkNIyMqHwseIiKqsPgrWXj604PI0xnFtvvbNsLcB1qz2KFajQUPERFVSPK1XIxccxBZBQaxrVcrP3zwSFso5Cx2qHZjwUNERHd0+WY+nlgdi2s5hWJbdKA3PnqiHZQKfpRQ7cd3KRER3db1nEI8uSYWl27mi21tAzyx+qkOcFYqbBgZUcWx4CEionJl5usx6tODSEzPFdta+rnj82c6wcNZacPIiKzDgoeIiMqUrzNi9Of/IO5yltjW1NsV68dEo56byoaREVmPBQ8REVnQGUwYt/4wDp2/Ibb5a9T4ckw0/DXONoyMqHJY8BARkYTBaMKkb49g95l0sa2eqxLrR0ejiberDSMjqjwWPEREJDKZBLz+43FsOZ4qtrmrnbD22U5o6e9hw8iI7g4LHiIiAgAIgoC5m+Ox4fBFsU3tJMfqpzqgTYCX7QIjqgIseIiICADw4Z9n8eneJPGxk1yGj59sj85BPjaMiqhqsOAhIiKs2ZOExX+cFR/LZMCiRyPRs5WfDaMiqjoseIiI6rjv/knB25tOStrmPRiB+9s2slFERFWPBQ8RUR225fgVTPvxmKTtjYGheKxTUxtFRFQ9WPAQEdVRf52+ipe/OQKTUNw2sVcLjI0Jsl1QRNWEBQ8RUR30T3IGxq8/DL2xuNp5+p7meKVviA2jIqo+LHiIiOqYE5cy8exn/6BAbxLbHmrXGLMGh0Emk9kwMqLqw4KHiKgOSbiag1GfHkR2oUFs6x/uj4UPt4FczmKHHJeTrQMgIqKacelmPkZ+/h8ycnViW/eW9bHksSg4Kfj3Lzk2vsOJiOqAGwVGjF5/FKlZBWJbu6Ze+GRke6idFDaMjKhmcISHiMjB3czX461dN3Ahq3gaK7ShBp890wmuKn4MUN3AER4iIgeWU2jA+K+OSYqdwPpuWPdsJ3i6KG0YGVHNYsFDROSgCvRGPLfuEI5dyhLbGnk6Y/2YaPh6qG0YGVHNY8FDROSA9EYTJnx1BPvOXRfbvF2VWD8mGo29XGwYGZFtsOAhInIwJpOAVzccxR/xaWKbq1KGVU+2RZCvuw0jI7IdrlYjInIggiDgzV/i8PN/l8U2F6Ucb3TzQmgDDxtGRmRbLHiIiBzIu7+fxhcHzouPVQo5lj4SgXq6tNvsReT4rJrS2rVrF5588kl07twZrVu3Ru/evTF//nxkZ2dLttuxYweGDBmCiIgI9O/fHz/88IPFsXQ6Hd555x107doVkZGReOaZZ5CYmHh32RAR1WEr/jqHj/46Jz6Wy4Alj0XinmBvG0ZFVDtYVfDcvHkTbdq0wZw5c7BmzRo888wz+Pnnn/Hyyy+L2xw6dAgTJkxAZGQkVq1ahQEDBuCNN97A1q1bJceaO3cuNmzYgMmTJ2Pp0qXQ6XR4+umnLYonIiK6s/UHzuOdrackbQuHtcV9rRvaKCKi2sWqKa2hQ4dKHkdHR0OlUmHmzJlIS0uDv78/VqxYgTZt2uCtt94CAHTu3BkpKSlYsmQJ7rvvPgBAamoqvv/+e7z55psYNmwYACAiIgI9e/bEN998g7Fjx1ZFbkREdcLG/y5h5sYTkrbZ94dhWPsAG0VEVPvc9VlaXl5eAAC9Xg+dTofY2FixsCkycOBAnDt3DhcvXgQA7NmzByaTSbKdl5cXunbtit27d99tSEREdcYfJ9PwyndHIQjFbf/rG4KnuwbaLiiiWqhSi5aNRiMMBgMSEhKwfPly9OrVCwEBAUhISIBer0dQUJBk++DgYABAYmIiAgICkJiYCB8fH3h6elps9/3331cyFTNBEJCXl3dXx6hJ+fn5kv87IkfPkfnZP3vNMTbpBl746hiMpuJq5+nOTfBs50aS34P2ml9FOXp+gOPnKAgCZDJZtT5HpQqenj17Ii3NvOK/e/fueP/99wEAmZmZAACNRiPZvuhxUX9WVhY8PCxPj9RoNOI2laXX6xEfH39Xx7CF5ORkW4dQ7Rw9R+Zn/+wpx7MZOszedQM6Y3Gx0yfQBYMDdDh16lSZ+9hTfpXh6PkBjp2jSqWq1uNXquBZuXIl8vPzkZCQgBUrVmD8+PH47LPPqjq2SlEqlWjRooWtw6iw/Px8JCcno3nz5nBxccyrnzp6jszP/tlbjmev5mDer0dQYCgudu4L88O7D4VBIbf8K9ne8rOWo+cHOH6OZ8+erfbnqFTB06pVKwBAVFQUIiIiMHToUGzfvl0sNEqfaZWVZb6PS9EUlkajQU5OjsVxs7KyLKa5rCWTyeDq6npXx7AFFxcXu4zbGo6eI/Ozf/aQ4/nruRjz5TFkFRTfDPRerS+WPN4eKqfbL8u0h/zuhqPnBzhujtU9nQVUwaJlrVYLpVKJCxcuoGnTplAqlRbX0yl6XLS2JygoCNeuXbOYvkpMTLRY/0NERGZXMvPxxOpYpGcXim2dAr2x4ok7FztEdd1d/4QcPXoUer0eAQEBUKlUiI6Oxu+//y7ZZsuWLQgODkZAgPkUyW7dukEul2Pbtm3iNpmZmdizZw9iYmLuNiQiIodzPacQT66OxcUbxYtWIxp7Ys1THeCiUtgwMiL7YNWU1oQJE9C6dWtotVo4Ozvj1KlTWLNmDbRaLfr06QMAeP755zFq1CjMnj0bAwYMQGxsLDZt2oRFixaJx2nQoAGGDRuGhQsXQi6Xw9/fH5988gk8PDwwYsSIqs2QiMjOZRXo8dRnB3EuPVdsa+HnjrXPdoKHs9KGkRHZD6sKnjZt2mDLli1YuXIlBEFA48aNMXz4cIwePVpcXd2hQwcsXboUixcvxvfff49GjRph7ty5GDBggORYM2bMgJubG95//33k5uaiXbt2+Oyzz8o8e4uIqK7K1xkx5vNDOHEpS2wLqOeC9aOj4e1WvWe1EDkSqwqe5557Ds8999wdt+vduzd69+59221UKhWmTp2KqVOnWhMCEVGdoTOY8PyXh3EwOUNs8/NQ48sx0Wjg6WzDyIjsD1e5ERHVQkaTgMnf/oe/TqeLbV6uSnwxOhrNfNxsGBmRfWLBQ0RUywiCgOk/Hsfm41fENjeVAmuf6QRtA077E1UGCx4iolpEEAT83+Z4fHsoRWxTOcmx+qmOaNvEy3aBEdk5FjxERLXI0h0JWL0nSXzsJJdhxRPt0CXYx4ZREdk/FjxERLXEZ3uT8MH2M+JjmQx4/5G26B3qb8OoiBwDCx4iolpgw6EUzPn1pKRt7gOtMTSysY0iInIsLHiIiGxs64krmPrDMUnbtAGt8ER0MxtFROR4WPAQEdnQ7jPpmPj1EZiKb3yOF3sGY3yPYNsFReSAWPAQEdnIoeQMPPfFIeiNxdXOqC7NMKWf1oZRETkmFjxERDYQdzkTz3z+Dwr0JrHtwajGmH1/OGQymQ0jI3JMLHiIiGrYufQcjFpzENkFBrGtb5g/3h3WBnI5ix2i6sCCh4ioBl26mY+Rq2NxPVcntnVt4YOlj0XBScFfyUTVhT9dREQ1JD27EE+ujsXlzAKxLbKJF1aO7ABnpcKGkRE5PhY8REQ1IDNPj5FrYpF0LVdsa9XAA58/0xFuaicbRkZUN7DgISKqZrmFBjz9+UGcSs0W25r7uGLd6E7wclXZMDKiuoMFDxFRNSrQG/HcF4dw5MJNsa2hpzPWj4mGn4ez7QIjqmNY8BARVROD0YSXvj6CvQnXxTYfNxW+GB2NgHquNoyMqO5hwUNEVA0K9Ea8+v0xbDuZJrZ5qJ2w9tlOaOHnbsPIiOomrpQjIqpiO06lYfYvJ3EhI09sc1bK8ekzHdG6sacNIyOqu1jwEBFVkZSMPLy16SS2lxjVAQClQoZPRnZAx+beNoqMiFjwEBHdpUKDEat2J2LZzgTJrSIAwNdDjYXD2qBHiK+NoiMigAUPEdFd2X0mHW/+Eie5vg4AKOQyPNWlOSb3bQkPZ6WNoiOiIix4iIgq4UpmPt7edBJbjqda9HVsXg9vDW2N0IYaG0RGRGVhwUNEZAWdwYRP9yZhyZ9nkaczSvp83FR4fWAoHm7XmHc8J6plWPAQEVXQvnPXMGtjHBKu5kja5TLgyc7N8L9+Wni6cPqKqDZiwUNEdAdpWQX4v83x+OXoZYu+yCZemPtAa55uTlTLseAhIiqH3mjC2n3JWPzHWeQUGiR99VyVmDagFYa3bwK5nNNXRLUdCx4iojIcTMrArI0nJDf8BACZDBjRsSle669FPTfe+JPIXrDgISIqIT27EPN/i8eP/16y6Ito7Im3H2iNyCZeNR8YEd0VFjxERACMJgFfHryIJX8lIbtAOn3l6aLEq/21eKxTUyg4fUVkl1jwEFGd919KJt748zqSbqZZ9A1vH4BpA1rBx11tg8iIqKqw4CGiOut6TiHe2XoK3x26aNEX2lCDt4eGowPvf0XkEFjwEFGdYzQJ+OafC1i49TQy8/WSPg+1E17pF4KRnZvBSSG3UYREVNVY8BBRnXLs4k3M/PkEjl7MtOi7P8IfM4e0hp+Hsw0iI6LqxIKHiOqEm3k6vPv7aXx18AIEQdrXwtcNo8LVGBYTBldXFjtEjogFDxE5NJNJwPeHL2LB1lPIyNVJ+txUCkzqE4JHovyQcOa0jSIkoprAgoeIHFbc5UzM/PkE/r1w06JvcJuGmDEoDA08nZGXl1fzwRFRjWLBQ0QOJzNfj0Xbz2Dd/mSYSk1fBfu64a2hrdG1RX3bBEdENsGCh4gchiAI+OnIJczbcgrXcgolfS5KBV7q3RKjuwVC5cSzr4jqGhY8ROQQTqVmYdbPcTiYnGHRN6B1A8wYHIbGXi42iIyIagMWPERk17IL9Pjwj7P4bF8yjKXmr5r7uGL2kHDcq/WzUXREVFuw4CEiuyQIAn49dgVzN53E1Wzp9JXaSY4JPVtgbEwQnJUKG0VIRLUJCx4isjsJV7Mxa2Mc9p27btHXJ9Qfb94fhiberjaIjIhqKxY8RGQ3cgsNWLLjLNb8nQRDqemrJt4umH1/OHqH+tsoOiKqzawqeH777Tf88ssviIuLQ1ZWFpo1a4aRI0fi4YcfhkwmE7fbsGEDVq9ejcuXLyMwMBCTJ09Gz549JcfKzs7G/Pnz8ccff0Cv16N79+6YMWMG/Pw4105EUoIgYOuJVLy16SSuZBZI+lQKOcbfG4wX7g3m9BURlcuqgufzzz9H48aNMW3aNNSrVw/79u3DzJkzkZqaigkTJgAANm/ejJkzZ2L8+PHo3LkztmzZggkTJuDLL79EZGSkeKxJkyYhISEBs2fPhlqtxuLFizF27Fj88MMPcHLiwBMRmSVdy8WsjSfw99lrFn09QnwxZ0g4mtd3s0FkRGRPrKosVqxYAW9vb/Fxly5dcPPmTXz22Wd44YUXIJfLsWTJEgwaNAiTJk0CAHTu3BlnzpzB8uXLsWrVKgDAkSNHsGfPHqxZswbdunUDAAQGBmLgwIHYtm0bBg4cWEXpEZG9ytcZ8dFfCfhkVyJ0RpOkr5GnM2bdH47+4f6S0WUiovJYdfWtksVOkdDQUOTk5CAvLw8pKSlITk7GgAEDJNsMHDgQ+/fvh05nvo/N7t27odFo0LVrV3GboKAghIaGYvfu3ZXJg4gcyPaTaei7aBeW7kiQFDtKhQwv3BuMP/7XA/e1bsBih4gq7K7njg4fPgx/f3+4u7vj8OHDAMyjNSUFBwdDr9cjJSUFwcHBSExMRGBgoMUvq6CgICQmJt5tSERkpy5cz8OcX+Pw56mrFn1dW/hgzpDWaOHnboPIiMje3VXBc+jQIWzZsgVTp04FAGRmZgIANBqNZLuix0X9WVlZ8PDwsDiep6cnTpw4cTchQRAEu7oRYH5+vuT/jsjRc2R+d6/QYMSavRewau8FFBqk01d+HipM7dcS94X5QiaTVcvPN19D++bo+QGOn6MgCNU+Ylvpgic1NRWTJ09GdHQ0Ro0aVZUx3RW9Xo/4+Hhbh2G15ORkW4dQ7Rw9R+ZXOf9eKcSaI1lIzTVK2hUyYFBLVzwS5g4X+XWcOmV5zZ2qxtfQvjl6foBj56hSqar1+JUqeLKysjB27Fh4eXlh6dKlkMvNS4E8PT0BmE859/X1lWxfsl+j0SA1NdXiuJmZmeI2laVUKtGiRYu7OkZNys/PR3JyMpo3bw4XF8e8z4+j58j8KufSzQIs+P0s/jx9w6KvYzMvzBgQgpZ+NXP2FV9D++bo+QGOn+PZs2er/TmsLngKCgowbtw4ZGdn49tvv5VMTQUFBQEAEhMTxa+LHiuVSjRp0kTcbv/+/RZDWElJSQgJCal0MgAgk8ng6mp/V1h1cXGxy7it4eg5Mr+KKTQYsfrvJCzdcRYFeun0VX13NWYMCsXQyEY2WZDM19C+OXp+gOPmWBM/71adpWUwGDBp0iQkJiZi9erV8PeXXtG0SZMmaN68ObZu3Spp37JlC7p06SIOV8XExCAzMxP79+8Xt0lKSsLJkycRExNT2VyIqJbbc/YaBiz+G+/+flpS7MhlwDNdm2PHlB54IKoxz74ioipn1QjPnDlzsHPnTkybNg05OTn477//xL6wsDCoVCpMnDgRU6ZMQdOmTREdHY0tW7bg2LFjWL9+vbhtVFQUunXrhunTp2Pq1KlQq9VYtGgRtFot+vXrV2XJEVHtcCUzH3M3xWPz8SsWfe2b1cPbQ1sjrJGmjD2JiKqGVQXP3r17AQALFiyw6Pvzzz8REBCAwYMHIz8/H6tWrcLKlSsRGBiIZcuWISoqSrL94sWLMX/+fMyaNQsGgwHdunXDjBkzeJVlIgeiN5rw6Z4kfPjnWeTppIuSfdxUmDagFR5uFwC5nCM6RFS9rKouduzYUaHthg8fjuHDh992Gw8PD8ybNw/z5s2zJgQishP7z13HrI0ncPZqjqRdJgOejG6GKf208HRV2ig6IqprOJxCRFXqalYB/m9LPDb+d9mir20TL8wd2hoRAXd3NiYRkbVY8BBRlTAYTVi7/zwWbT+DnEKDpM/LVYmp97XCox2acPqKiGyCBQ8R3bVDyRmY8fMJnErNtuh7rFMTvNq/FbzdqveiYkREt8OCh4gq7VpOIeZvOYUf/r1o0de6sQZvD22NqKb1bBAZEZEUCx4isprRJOCr2PN49/fTyCqQTl9pnJ3wan8tHo9uBgWnr4iolmDBQ0RWOXLhBmZuPIETl7Is+oa1D8C0Aa1Q311tg8iIiMrHgoeIKuRGng5v/ZaAb/5Jsehr1cADbz/QGh2be9sgMiKiO2PBQ0S3ZRIEbEvMwzebYpGZL52+clc74ZW+IRjVpRmcFFbdqYaIqEax4CGich2/mInpPx7F8cuWZ189ENkI0weGwk/jbIPIiIisw4KHiCxk5unx7rZT+DL2AgRB2tfSzx1vDW2NLsE+tgmOiKgSWPAQkchkEvD9vxex4LdTyMjVSfpclApM7tsSz3QNhJLTV0RkZ1jwEBEAIO5yJmZtjMPh8zcs+u4JcMbcYVEIasBFyURkn1jwENVxWQV6fLDtDNbtT4ap1PRVkK8bpvdvgXqFaWjAtTpEZMdY8BDVUYIg4Of/LuH/Np/CtZxCSZ+zUo6JvVpiTPdAGHWFiI9Ps1GURERVgwUPUR10Ji0bM38+gdikDIu+/uH+mDk4DAH1XAEAeTqLTYiI7A4LHqI6JKfQgA//OIPP9ibDUGr+qpmPK2YPCUdPrZ+NoiMiqj4seIjqAEEQsOnYFczdfBJpWdLpK7WTHC/c2wLjegTBWamwUYRERNWLBQ+Rg0u4moPZv8RhT8I1i75erfww+/5wNPVxtUFkREQ1hwUPkYPK0xmwdEcCVv+dCL1ROn3V2MsFs4eEo2+Yv42iIyKqWSx4iByMIAj4PS4Vb/16EpczCyR9KoUc43oE4YV7W8BFxekrIqo7WPAQOZCka7mY/Uscdp1Jt+iLCfHFnCHhCKzvZoPIiIhsiwUPkQMo0Bvx0c4EfLwrETqjSdLX0NMZswaH4b7WDSCTyWwUIRGRbbHgIbJzf5xMw+xf43DxRr6k3Ukuw5juQXipdwu4qvijTkR1G38LEtmplIw8zPk1Dn/EX7XouyfYB28NDUcLPw8bREZEVPuw4CGyMwV6I1buTsTynQkoNEinr/w1aswYFIbBbRpy+oqIqAQWPER25K/TVzH7lzgkX8+TtCvkMjzbtTle7hMCdzV/rImISuNvRiI7cOlmPt7+9SS2xqVa9HVq7o23H2gNbQNOXxERlYcFD1EtpjOYsHpPIpb+mYB8vVHSV99dhekDQ/FgVGNOXxER3QELHqJaam/CNczceAKJ6bmSdrkMGNWlOSb3DYGni9JG0RER2RcWPES1TGpmAeZuPolNx65Y9LVr6oW3H2iN8EaeNoiMiMh+seAhqiX0RhM+35uMxX+cQa5OOn3l7abCtPtaYVj7AMjlnL4iIrIWCx6iWuBA4nXM2ngCZ9JyJO0yGfB4p6Z4tb8WXq4qG0VHRGT/WPAQ2dDV7ALM33IKPx25ZNHXNsATbz/QGm0CvGo+MCIiB8OCh8gGDEYTvjhwHh9sO4PsQoOkz9NFian3tcKjHZtAwekrIqIqwYKHqIYdPp+BGT/HIf5KlkXfox2aYOqAVvB24/QVEVFVYsFDVEOu5RTind9OYcPhixZ94Y00ePuB1mjXtJ4NIiMicnwseIiqmdEk4KuDF/Du1lPIKpBOX3k4O+HV/lo8Ed2M01dERNWIBQ9RNfov5SZm/nwCxy9lWvQ93C4A0wa0gq+H2gaRERHVLSx4iKrBjVwdFv5+Ct/8kwJBkPa1auCBt4a2RqdAb9sER0RUB7HgIapCJpOA7w6l4J2tp3AjTy/pc1c7YXLfEDzVpRmcFHIbRUhEVDex4CGqIievZGPu1iP4L+WmRd+Qto3wxqBQ+Gucaz4wIiJiwUN0tzLz9Vj1bxa2JR6CqdT0VQs/d7w1NBz3BNe3TXBERASABQ9RpQmCgB/+vYR5m08io9T0latKgZd6t8SzXQOhcuL0FRGRrbHgIaqE+CtZmLXxBP5JvmHRNzCiAWYMCkMjLxcbREZERGWxuuA5f/481qxZg6NHj+Ls2bMICgrCpk2bLLbbsGEDVq9ejcuXLyMwMBCTJ09Gz549JdtkZ2dj/vz5+OOPP6DX69G9e3fMmDEDfn5+lc+IqBplF+ixaPtZrN2fDGOp+atm3i54+4EIxIT42ig6IiIqj9Vj7WfPnsWuXbvQrFkzBAcHl7nN5s2bMXPmTAwYMACrVq1CZGQkJkyYgP/++0+y3aRJk7B3717Mnj0b7733HpKSkjB27FgYDIYyj0tkK4IgYON/l9Dr/V34dG+SpNhxdpLj8dbu2Di+E4sdIqJayuoRnl69eqFPnz4AgGnTpuHEiRMW2yxZsgSDBg3CpEmTAACdO3fGmTNnsHz5cqxatQoAcOTIEezZswdr1qxBt27dAACBgYEYOHAgtm3bhoEDB1Y2J6IqdTYtGzM3nsCBxAyLvn5h/ni1TyCyriRzrQ4RUS1m9W9oufz2u6SkpCA5ORkDBgyQtA8cOBD79++HTqcDAOzevRsajQZdu3YVtwkKCkJoaCh2795tbVhEVS630ID5W+Ix4MO/LYqdpt6u+PTpDlg5qgMac60OEVGtV+WLlhMTEwGYR2tKCg4Ohl6vR0pKCoKDg5GYmIjAwEDIZNL7BwUFBYnHILIFQRCw5Xgq3t50EqlZBZI+lZMcL9wbjPE9guGsVNgoQiIislaVFzyZmeZ7Bmk0Gkl70eOi/qysLHh4eFjs7+npWeY0WUUJgoC8vLxK71/T8vPzJf93RPaUY9K1PPzf1jPYl2h59lVMC29Mvy8ETb1dYNIXouhMdHvKrzIcPT/A8XNkfvbP0XMUBMFiAKSqOdxp6Xq9HvHx8bYOw2rJycm2DqHa1eYcCwwm/BCfi19O58JQ6uKBvq5yPBupQcdGSuSmJSM+rexj1Ob8qoKj5wc4fo7Mz/45co4qlapaj1/lBY+npycA8ynnvr7FZ6xkZWVJ+jUaDVJTUy32z8zMFLepDKVSiRYtWlR6/5qWn5+P5ORkNG/eHC4ujrkWpDbnKAgC/jx9DfN3nMWVzEJJn1Ihw7NdmuK57s3gcpvpq9qcX1Vw9PwAx8+R+dk/R8/x7Nmz1f4cVV7wBAUFATCv5Sn6uuixUqlEkyZNxO32799vMYyVlJSEkJCQSj+/TCaDq6trpfe3FRcXF7uM2xq1Lcfz13Px5i9x+Ot0ukVf95b1MWdIOIJ83St8vNqWX1Vz9PwAx8+R+dk/R82xuqezgEqcpXUnTZo0QfPmzbF161ZJ+5YtW9ClSxdxyComJgaZmZnYv3+/uE1SUhJOnjyJmJiYqg6LSFSgN2LR9jPou2i3RbHTQOOMj55oh3XPdrKq2CEiotrN6hGe/Px87Nq1CwBw6dIl5OTkiMVNp06d4O3tjYkTJ2LKlClo2rQpoqOjsWXLFhw7dgzr168XjxMVFYVu3bph+vTpmDp1KtRqNRYtWgStVot+/fpVUXpEUjtOpWH2LydxIUO6sN1JLsPo7oF4qVdLuKkdbmkbEVGdZ/Vv9uvXr+Pll1+WtBU9XrduHaKjozF48GDk5+dj1apVWLlyJQIDA7Fs2TJERUVJ9lu8eDHmz5+PWbNmwWAwoFu3bpgxYwacnPiBQ1UrJSMPb206ie0nLVccdw7yxttDW6Olv+VZg0RE5BisriwCAgJw+vTpO243fPhwDB8+/LbbeHh4YN68eZg3b561YRBVSKHBiFW7E7FsZwIK9CZJn5+HGm8MCsWQto1qZP6YiIhsh0Mp5LB2n0nHm7/EIelarqRdIZfh6XuaY1KflvBwVtooOiIiqkkseMjhXL6Zj7c3ncRvJywve9CxeT28NbQ1QhtqytiTiIgcFQsechg6gwlr9iRhyZ9nka83Svrqu6vw+oBQPNSuMaeviIjqIBY85BD2JVzDrF/ikHA1R9IulwEjOzfDK/208HTh9BURUV3FgofsWlpWAeZujsevRy9b9EU19cLbQ1ujdePKX7mbiIgcAwseskt6owlr9yVj0fYzyNVJp6/quSoxbUArDG/fBHI5p6+IiIgFD9mh2MTrmLUxDqfTsiXtMhnwWKemeK2/Fl6u1XsTOiIisi8seMhupGcXYv6WePx45JJFX5sAT7w9tDXaNvGq+cCIiKjWY8FDtZ7BaMKXsRfw3rbTyC4wSPo8XZR4tb8Wj3VqCgWnr4iIqo8gAEYd4KS2dSSVwoKHarXD529g5s8ncPJKlkXfIx0CMPW+VvBxt88fPiKiWsNoAHLSgKzLQPZl8/+zLgFZV0q0XQG6vAj0edPW0VYKCx6qla7nFOKdrafw3aGLFn2hDTWY+0A42jfztkFkRER2xqgHFKUuyyEIwHejbhU1l83FjmAqe/+Ssq9UT4w1gAUP1SpGk4Bv/rmAhVtPIzNfL+nzUDvhf/1C8GTnZnBSyG0UIRFRLVKQVTwak33FcmQm6xLg4gW8dES6n0wGXNgP5KZb93xZlmso7QULHqoVzl/Pxc5TV/HDv5dw/FKmRf9DUY0xbWAr+Hk42yA6IqIaJgjmoqRUm+q3V4DcomLmMqDLKXv/kvR5ZR9P0+j2BY/S1byNphGgaQx4NAT8w63PpZZgwUM2UaA3IjYpA3+dvoq/Tqdb3OCzSIi/O94e2hrRQT41HCERUTUxGoCc1FujMEUjM6VGZZQuwIux0v1kMsgTdwC5adY9n6EAyL8BuJZaBtD0HsDNr7ig0TQ0f+1xq8hx9rQskuwYCx6qMRdv5CP26FXsPJ2O/eeuW9zvqiQ3lQKT+4bgqXuaQ8npKyKyZ1tfB25euLX490rF1sso1GWOyggeDe5Q8MgAd/8SIzO3/snK+D06YIH1udgxFjxUbQoNRvyTdAPbT1zCHyfTcSnb8u7lpfl6qNEvzB8Te7VEA09OXxFRLSEIQGFW8VRS0T/xjKYrgFwBjNtluW/cT9Yv9jUWAnkZgJt0dNvUoC0UqltTTR4Nb43MlChs3P0tFygTABY8VMUu3czHX6evYuepdOw7dw15uvJHcQDzzT3bN6uHe7V+6BHii/BGGt7NnIhsb+e84lGZon/6sqfeRQoVYDIB8lKjKZpGdy54lG6AZ2NpEVPG70J9v3egdHW1MhkCWPDQXdIZTDh0PgN/nU7HX6ev4kzanRfQ1XdXoUeIH3q28kX3Fr7wdOVfI0RUTUqvl5GMyty66fCzWy33O7Le+jOSjDog7zrg7itt9w0FDLriNTJFC4BLrp1RaxxqvUxtxIKHrHYlM18scPYmXEdOoeG228sAhPgo0S8iAP1aN0Z4Iw1v6klEVc7pn4+B3NQShU0F1svIncoflblTwSOTF6+XKRqZKcsDy61LhKoFCx66I73RhH/P38DOW0XOqdTsO+7j7aZCjxBf3Kv1RccAd1w5n4DQ0OZw5VAsEd2JZL1MqbOXsq8AJgMw8ieL3ZwOr7Z+VMZkMJ+a7eEvba8XCORclZ69pCk15eTuDyj4MWov+EpRmdKyCrDrdDr+OnMVf5+5huw7jeLIgDYBXuip9cW9Wj+0aewpjuLk5eXBfq/NSUTV6vBa4OZ5y8XAt1svI1MAJqN5kXAJgkcFRmUAQOUuvb5MWSNAD6+yMhGq7VjwEADzDTqPpNwUFxyXde+q0rxclYhp6YuerXwR09KX97QiquuMeiA7tcR1ZUoUMIZC4LGvLPf5+z3z4mBrCMZboy8Npc3uDQFXH+m1ZMoaoXHW3EWSZK9Y8NRh6dmF2HUmHTtPX8XfZ9KRVXD7URwAiGjsiZ5aX/TQ+iGyiRfvUE5UF534Abhx3vJWBjlpAISy95HJzQuIS08BaRpXrOCRyQH3BrcKl4bmqahSdENWwMnN3fp8qE5gwVOHGE0C/rs1ivPX6fQyb+FQmsbZCTEhvuJp474eHMUhciiCABRklnFdmUuAPh94eLXlPn++DdxIsvJ5TOaCyLPUwl5NI/Pp3OWdvVS0XsbN787rZcq6uB7RLSx4HNz1nELsPpuOnafSsftsOm7m6e+4T3gjDe7V+qLnrVEc3qiTyP5p0mLhVBAL5KdbnpqtzytnLxnwwArLC9lpGlW84Cm5Xsaos+wfuhx4eA1PyaZqx4LHwZhMAo5dysTOU1fx15l0HLt4E0I5I8xFPNRO6B5SH/dq/XBviC/8NLzCMVGtZ9TfmlK6Ij0NuzALGLLUYvMmccugyrX2TteCeU2OVxNps6aR+f+u9YtHYSRX/W1Y8fUyShcrYyKqHBY8DuBGrg67z6bjr9Pp2HUmHRm5ZfwVVUqrBh64V+uHnlpftGtWj/erIqqNzu+7dbXfMk7NzrmKctfLDHwfcFJJmvTOvnCuaMFTcr2MoaCM478LDFkGKPnHEdkPFjx2yGQScOJyJv46bV5wfDTlJkx3GMVxVzuhawsf9NT6oYfWFw09+VcVUY0TBKDgpuUp2AU3gQHvWG7/y0TgeoL1z5N9BajXTNKkc65v/kKhtryxpEepx3daL+NSz/qYiGyMBY+dyMzTlxjFuYprOXcexQnxdxcLnA7NvKFy4igOUbVLOWgelcm5ar6lQXZqqdOz88ver88cyxETTSPrCh61xjyNpLO8hs3FsPFweehDuHo35noZqpNY8NRSgiAg7nKW+bTxU1fx74UbdxzFcVUp0LVFfdx76+J/jb04ikN0V0xGIPea+eyinDRz8VL0dUEm8NBKy31+fRm4etL658q+AngHSttK3qqg5HqZskZl7rBexuDsDbh4s9ihOosFTy2SVaDHnrPXsPPUVew6k46r2YV33KeFnzvuDfFFz1Z+6NC8HtROijvuQ0RSiuPfoFHCIaiSTUDB9eLCJjf99vdhGrwYUJW6XYq7n3UFj0wBeDQwLzYured0oMdUczHD9TJEd4UFjw0JgoDTaTn48VQOTsf+iyMXs2C8wzCOs1KOrsH1cW8r8xlVTbx5byoiAOb1Mfk3LEdistOKvy7MAsbttthVeWglGqbHW/+cOamAd5C0zb1BiQO73rq5ZKmzl0qO0Lj7WdwiQeTV1PqYiKhMLHhqWHaBHnsTrosX/0vNKuMMiFKC6ruhx63r4nQK9IazkqM4VIeUdXVeANj+JnDt7K11MrcKGtOdrzOFwhxALb0ar+DmC1hT8Lj6mAsbfRk/vz1fB2JeNd+MUu1R8WMSUbViwVPNBEHA2as55uvinE7HP8kZMNxhFEftJEeXYPMZVfdqfdHMx62GoiWqIYIAFGaXWhtzVVq8FP3TFwDTL1muPUn4E0g7bv1z56SVUfD4wyRXAu5+kHs0NI/KePibixp3P/OUk7uf+bGbr8Up3xL1mlsfExFVOxY81SC30IC9Cdfw15l0/HXqKi5n3nkUp0k9F/QO9UcPrS+6BPlwFIfsk8kI5F0H8jIAv1aW/V8/DlyNMxc35V7dtwyF2ZYLcj38K1bwqD1vFS+3/pUxfaTrvxDxgeMRGhYGV1dOExM5IhY8VUAQBJxLzxGvi/NP0g3ojLdZ6AhA5SRH5yAfdA30RCPZTfTuFMFftFRryYw6yG5eAK5nlVofU2qtTG66+U7WTs7AG6mWozKZF4AbydYHkJNmWfD4tjI/X1Eh49Gg+GtxhMa/YlfydXLm2UtEDo4FTyXl6QzYf+46dt5ai3PxRjnX1ighoJ4Lemr90LOVLzoH+cBV5YS8vDzEx+fUQMREpZRe5FuYBYQNtdjMeU0M2l0/a92xDQXm4zl7StvdGwC4zahM0SLfkgWLu7/5+jKl9f8/62IiojqNBU8FCYKApGu54ihObFIGdIbbj+IoFTJEB/qI18UJ9nWDjH9FUk3Iulw8+nK79TElb+aoUAGhQyxHOpwqeTp0dpplwRN0L+BWv4yipoH5a5U7R1qIqFqw4KmAvQnXMPPnE0i8Znn10tIae7mIZ1TdE+wDNzW/xVQFBAHQ5dwqWFKLp5D0eUDMFMvtV/cx33PJGkad+RYHpW4bILj5o8xRGYXqVrHiV1ywSKaUGljuc88E62IiIqoi/DSugKk/HCt3yspJLkPH5t7o2co8itPSz52jOFQ553aaixTx2jG3RmaKRmrKWuSrUAHd/2c5KuLub33BA5ift1TBY2g9HFfUQfANioDau0nxWhmXehyNISK7wYKnArzdVJKCp4HGGT1b+aJHiB+6tvCBh7PShtFRraQvsCxYiqaXjHrgoU8s99k4Aci6aN3zGHXmdTiu3tJ2d3/pY5ncfENI8RTrkgt9S4zQaAIsn6LVEKQJLeEdGgpwYT0R2SkWPBWwalQHfLH/PNydnXCv1hdafw+O4pDZgY+hvJaI5lcSoD5aAOSlF99nqTxyJ+CBFYC81M1cPfytL3icXMyngJcueLpNBjqOLi5qXH3Kv5ovEVEdwIKnAvw1zpjSX2vrMKi6GPW3FvXe5iJ4EICxOyz33b8cyswL8LHm+UwGID/DvHi3pJK3JCi6km/pi96VXOTr7me+km9ZxXfTaGsiIiJyeCx4yDEJAqDPt7yxIwBsmmy+FkxRMZN3HcAdbkUvU5gvqld6lMTD33xtmYqQK4vPTCprPc6ABcDAd+98JV8iIrIaCx6yLyYTkHet1MLechb5qj2AKWcsj5HwJ3DzvHXPK9y6grC7n7Td3R+CygOFSi8ovQOg8GxUamSmxFqZOy3y5Y0iIQjmwrNoytgkmJCty4ZRMMJoMsJN6QZXpbmI1Rv1OJ913twnGNHYvTE81ebT4HP1uTiafhRGkxEmwYTw+uGo72IeUbuWfw37Lu+D0WSEQTCgZ5OecIX5mCk5Kfj73N8wmszHfET7iLhf3PU4/JLwi/h8L0e9DC9nLwDAvkv78P3Z72EwGWAUjHg35l0xzq3JW/HNqW/EHFf1XQWlwrzu7+eEn7ExYSMAwEnuhFX9VonbfX3qa2xL3gYA8FB5YEmvJWLfpyc+xd8X/wYA+Ln64Z2Yd8S+5f8tx6HUQwCAZppmmH3PbLFvydElOJ15GgDQyrsVpnaaKvbNPTAXiZmJAIBI30i81O4lse+NPW/gSu4VAECXhl0wts1Yse+Vv17BzcKbAICeTXpiZNhIse+FP15AgdF8pfmBgQMxLGSY2Pfs78+KXz/U8iEMDhoMAMjT52Hijoli3xOhT6BX014AgOv51/Ha7tfEvtERo3FPo3sAAOm6dCz9aykUCvMfJROjJiLSLxIAkHAjAfMPzhf3e7Xjq2jlbb4S+IlrJ7Do8CKxb1aXWWimaQYA+Cf1H3x89GOxb373+fBzNf8O2H1xN9bFrRP7Puj5ATQq8/Wifk/+HRtObxD7VvRZIb7mGxM24tdzvwIwv+Yf9y0+/jenvsEf5/8AYH7NF/Usjmv96fX4I+kPuF11Q0P3hpjXfV7x8Y+uwOG0wwCAph5NMavLLLFv0eFFiLseBwDQ1tPi1Y6vin3zYueJr3lb37aYGFX8fZ+5d6b4mndu2BljIsbA3tm04Dl37hzmzp2LI0eOwM3NDUOHDsWkSZOgUvGvW4dgKDRPFxVkQ1GYeetMI6V5SsdkNPeZDNJ/ciUQ0N7yWJ8NBDISzQWNYKzg8xeUPSrj7n/ngkcmN4+0lLxejFDGKNDwtcgv1CE+Ph6hoaFWXS279Id7gaEABYYCGAQDTIJJ/MUKmD+kMwoyYBJMkEEGrXfxFGvCjQSk5aXBKBihVqgR3bB4Ouuf1H9wMfsiTIIJnmpP9GnWR+z7Lek3XMy+CINgQCO3Rhjaoviig2vj1uJi9kUYBSMC3QMRhjCxb8HBBUjNTYVRMKKjf0eMCh8l9k3cMREZ+RkwCAb0b94fz7Yu/lB7cOODyNXnwmgy4vHQxzE6YjQAwGAyIPrLaJgEEwyCAZPbTxb3yyrMQvdvu4vHmNl5Jh7RPgIAuJp/FQ/+8qDY9073dzAwaCAA4GL2RYzbPk7sW957OWICYgAAF7Iu4I09b4h9QZ5BCPUINfdlX8CH/34o9vVu2lsseM5nnsdXp74S+0a3Hi0WPJdyL2H7+e1in86oEwuetNw08cMIAIQSo4mXcy7jUJq5OFHKpSc/XMi6IPZ5O0vXaCVnJot9TTyaSPrO3Twn9uXqpZfSSMhMEPtKi8+Ix7H0YwAAN6X0/n0nrp0QPxj9XaUL4o+mH8XVvKsAzN/Lkv69+q8YQ5v6bSR9/6T+I35dVLQAgFEw4mDqQfFx32Z9xa/1Jr2k74EWD4hf60w6HE4v/j7fKLghfp2jz5Hsl63LFr/OLMyU9OWVGH29XnBd0ldoKBS/Ts9LR2xqrPjYYDKIX6fmpkr6Sr7ml3IuiX2lX/PzWefFPovXPCsZ8bnxQC7QJF/6mp+9cRaxV8z7ZRVmSfpOZZwS+4wm6e/OuOtx4mvu4iS9Ivmx9GPia+7r4gtHYLOCJzMzE0899RSaN2+OpUuXIi0tDQsWLEBBQQFmzZp15wPUsAJDAa4XXBcf+7r4QqUwF2Z5+jxkFGSIff6u/mI1n6vPlfzgNXBrACfIgMIsZBdmIvXmFdzITMDly9kI8mwEuWAETAZkFmYiqyATEMzFQRO1F+DiDTSKxM2Cm+IPrMxQiIDkfbeKByOu67KQa8gDBCMUJgGN5WqxmLimz0WusQAQjFC6N0Sj3rPFuNJy01BgyAd+fRlqkxENBJn5LtUmA1JNhSgQ9IDJBGeTEQ0MtwoWkwGXYIDO1Rt4/Bu4Kd0kH9IpSyJgyDd/zwJMJrhuK75QY7KTE0y3Bjs8jSb4mMx9gpsvEsb8BpNgglEwwtfFF76uvkDWZeizr+AfZ2eYZEoYAQTr9QgwmH+A82QybHdzhRGAUQZ0yi9EM4MByL2GDKUSP579UfyrfYBvCwTKZIC7Py66avCF4SqMTs4wKJ3xdIuH0dw/CnCrj/gbZ7Dq+Cpxv6nQoejXzIErB7DsyDIYTUbojXqM8y/+cP0t6TcsPrxYHAn4eejP4sjD+pPr8cHhD2AUzCMP/z75r/heWXV8FVYeWwkAUCvUOPRk8QfTmuNrsD5+PQDzL8Jdj+4S+9aeXIufE34GYP7rbvNDm8W+r099LX4Qh3qHSgqen87+hP1X9gMAOjboKCl4fk/+Hcevma+9071Rd4R5Fxc8+y/vF38RuiulN+E8ef2k+OEXUT9C0nc55zLyDOYPk5IfOAqZAjpT8QUQTULx+0RRqlg1lih2FTIr+kr8opfL5OX2ld7PIBR/iFkTS8n9iKh2sFnB88033yA3NxfLli2Dl5cXAMBoNGLOnDkYN24c/P39b3+AGvbfyQ0Ye2Sh+PirQg0ijDLApMd+uQ6TXIt/wW28lo8gvQ4wGfGHtjtm5MSJfX8O/xN+manAJ93xi8YdC3xuVfEpQGxyClxv/dX/jZcGy+p5AQDkgoCjySlAy/7AE9/h0xOf4rO4zwAAHkp37DtzUjz+cp962KDxAAA0NBiwLeWy2LfQ1we/uZv/cmuRIcdPmC32zdk/B39fMg+RRxUUYN2Vq2Lf1IZ++NfZfLXdbnn5WHEjXex7qVEDnFHnAT8PwX3N78O7Pd4V+8Z6qXDJpxEA4OGsHMy+XlwUPtnIH5m3hp6fvpmF/924ae7IvYaHfnlI3O7ldi+bh1I9GqDwZjLGNSwuqF6/loHHs8235chy9cYM3+K/Sud5tUczv46Akxo3Cq5L/mpv1fNDBBYNkacfxVdbnhT7BrYdi+Ye5vfejYIbkr/an498Xvw6qzALR9OPio91vsUf2PmGfFzOLf6+60168WsBguSxUTBCCXPB4yQr/nEs/ZdYyQ9Uo1DxvpIf7hb7lfgAv+3zmW5zzNvsV7JwsXi+ErHIZDIoZAqx7XYFiKQYKl1kmCpWnDjJpb/2JEXNrWPKZXIoZApxFA4w/wVc36U+5DI5nGROkKP4+1BPXQ+h3qFQyBRQyBWS71Fj98bo2qhrcb4ontZs4tFEHN0oHVegZ6DYV7qwDPYKFvtK//WtracVR1VKj/60qtdKnPIJqRci6WtTv434PEXTPUUi/SLFkZ0WXi0kfe392+NmwU0x5pI6NeiEAoN5SqupRjpl26VhF/Hrxu6Nxa8VMoWkr+SIklKuROeGncXHRaNvAKCSq9DJrxPkCvP3vp5z8fWkPFQekv2Kpp4AwEvtJekrObrl4+wj6VM7qcWvfV19JaOpJV+/hm4NJX0lX/MAjwCxr/Rr3kzTTOwrGSMANNc0R5hbGNzc3NCg5AkOAFp6tRRHdppopK+51lsr/kyFeEtf83CfcLgozCM7Fu8H3zbieyvYKxiOwGYFz+7du9GlSxex2AGAAQMG4M0338TevXvx0EMPlb+zLehL3e/q2hlAd+tDztUFcC3xS6fgBqC/9UvUqIcFeSW/7SZ7+6vR+lP3ZRCgkMlhvPXBJn6IhdwHRf0WwI3ikQ1TlwlA+EjAzQ8KXSawoZfYZwgbCrQ0T3codDclz1HRv8zl8oqPBJhQ/gdxhT/AyykISsdS6WKonP2cZE4Wox71nOvBz8UPCrkC9dTSCxG28GoBlUIFJ5kTAjyk1+3p3LAzbhTcgEKusPjQHBo8FIXGQihkCkT6Rkr6xrUZB8jMsbT3L57SVClUmNl5plhIlBw18lR7YknPJVDIFVDIFJIP4kbujfDNoG/E4qOhW0OxT1tPiz+G/SHu565yh77A/HPawa8Djo46avH9AICYgBjsfGSnRTsA9GzaEz2b9iyzr0+zPpKRtZLuD74f9wffX2bfI9pHxOm70p4KfwpPhT9VZt+4tuPKbAeAFyJeKHfateR6ntLm3DOn3L6FMQvL7Su57qi0lf1WltnuqnQtt8/HxUeyzqkkX5UvlvZYWmZ+wV7B5e4XXj+83L6ODTqiY4OOZfbFBMSI06Sl9WveD/2a9yuzb0jwEAwJHlJm34hWIzCi1Ygy+57UPon2pvZlTp2X/GOstFfav1Ju3/To6eX2vd317XL77JXNCp7ExEQ8/PDDkjaNRgNfX18kJiZW+riCICAvr4wzYO6STn/7+2aVx2iQFjz5+fnI1+lRgfs3Wx5LX4jCvDzoDWUUUeUQFGrzGha50nxV3iIyheT7ZDQWfxgKag2MzVoBcgUEuRKC6QIA87aCewMY/PpAuHVMITsWMJqLQYPRIDmm4KwB9Oa/OnJ8IpATORBKZxdzLPGLAaP5Yo4G7WAUNBtiLgTlTpAfmCwWPAW6AvMx2z1nHhn5oZt4/AJXX+QpvQGdAbrCEveEApBfmC/GUtQnhxxymRwFhQVin0lvgo+zj/kDVaaASW8S+5RGJbReWvHDFgaIfe5yd3Rt2NVcNJgAtUyN/HxzPg3UDfBA0APiMQV98XuypUdLjAkbA4XM/EGsK9ABt17ODj4dMK39NHG/3NxccX1Pv8b90NqzNeQyOZRypeT7/FiLxzCw6UAoZAqoFWpJ36Q2k/Bi+ItQyBVwkjlJ+hZ0XgAZZOJzlO4Tv5f5+UhOThbzm9NR+uFXcr9pUdPK7ZvYemK5fU+FPFVu3+Amg8vti64fXW5foGuJ0QYDxOk0APCQeZhPzBMAfYFezK2goMAhr7FVlF/R/x2No+cHOH6OgiBU+8+eTBDKWolZ/cLDw/Hyyy/jueeek7QPHjwYUVFRePtt66vL48ePQ6fT3XnDSii8cRRXElZDkMkBmRwdTK7QyNUQZApclZlwXFEIAXIIMjk6ybzhKldBkCmQ7N4ARzTF0zBRHlFwNurgfWkHLgp5OGfKAmTm/bqogyCXKyHIFLhgysIFw41bfQp0cQuH0ckNhe4BuJB/Aam6VACAAnJEOwdDkCkgyBRILLyEq4YMADKoFGpEekSKz30u7xwy9OZpJRe5C1p7tBb7zuSeQabBfLE8d4U7Qt1Dxb74nHjk3CpqPJ08EeJWPPQZlxOH/FuFi7fSG0GuxYsWj2UfE9dm1FfVR3OX5mLff9n/iaMRfio/NHFuIumTQQYFFPBT+cFPbf7+CYKApPwksVjwcvKCh5N5+s4kmJBpyDRPRUABtVwtLggsvTiYiIhqH5VKhYiIiDtvWEkOd1q6UqlEixYt7ryh1UIReU/ZQ43Nbv0rS9tb/yy06QhfAK1u/fXcvHlzuLgUj/s0BtClrP0AhCK0nB5Aiw7l9t1uv+rsyy8jx8oes+TZQrVFWfk5EkfPD3D8HJmf/XP0HM+ePVvtz2Gzgkej0SA7O9uiPTMzE56enpU+rkwms+rU4NrCxcXFLuO2hqPnyPzsn6PnyPzsn6PmWBMj8JYr82pIUFCQxVqd7OxspKenIygoqJy9iIiIiKxns4InJiYG+/btQ1ZW8UWStm7dCrlcjq5du95mTyIiIiLr2KzgGTFiBNzc3PDiiy9iz549+OGHH7Bw4UKMGDGi1l2Dh4iIiOybzQoeT09PrF27FgqFAi+++CLef/99DBs2DNOmTbvzzkRERERWsOlZWsHBwfj8889tGQIRERHVATYb4SEiIiKqKSx4iIiIyOGx4CEiIiKHx4KHiIiIHB4LHiIiInJ4LHiIiIjI4bHgISIiIocnEwRBsHUQVeXff/+FIAhQqVS2DqXCBEGAXq+HUqmskZun2YKj58j87J+j58j87J+j56jT6SCTydCuXbtqew6bXniwqtnjm0Amk9lVgVYZjp4j87N/jp4j87N/jp6jTCar9s9whxrhISIiIioL1/AQERGRw2PBQ0RERA6PBQ8RERE5PBY8RERE5PBY8BAREZHDY8FDREREDo8FDxERETk8FjxERETk8FjwEBERkcNjwUNEREQOjwUPEREROTwWPEREROTwWPDUgN9++w3PP/88YmJiEBkZiaFDh+L7779H6fu2btiwAf3790dERASGDBmCnTt32ihi6+zatQtPPvkkOnfujNatW6N3796YP38+srOzJdvt2LEDQ4YMQUREBPr3748ffvjBRhHfndzcXMTExECr1eL48eOSPnt9DX/88UdotVqLf++9955kO3vNr8hPP/2EBx54ABEREYiOjsaYMWNQUFAg9tvze3TkyJFlvoZarRabN28Wt7Pn1/DPP//E8OHDERUVhW7duuHll19GSkqKxXb2muPOnTvx4IMPonXr1ujRoweWLFkCo9FosZ09vE/Pnz+PWbNmYejQoQgLC8PgwYPL3K4ir1V2djamT5+OTp06ISoqCi+99BKuXr1qfVACVbtHHnlEmDx5srB582Zh3759wnvvvSe0atVKWLp0qbjNpk2bBK1WKyxatEjYv3+/MHPmTCEsLEw4cuSI7QKvoJ9//ll45513hK1btwoHDhwQvvjiC6FTp07CM888I27zzz//CKGhocLMmTOF/fv3C4sWLRK0Wq3w22+/2TDyylm4cKFwzz33CCEhIcKxY8fEdnt+DX/44QchJCRE2L17t3DkyBHx3+XLl8Vt7Dk/QRCEjz76SIiKihI++eQTITY2Vti6davw5ptvCjk5OYIg2P979OzZs5LX7siRI8KkSZOEsLAw4fr164Ig2PdreODAAaFVq1bCtGnThL179wqbN28W+vXrJ/Tp00fIz88Xt7PXHI8cOSK0atVK+N///ifs3r1b+PTTT4U2bdoICxYskGxnL+/T7du3CzExMcLEiROFwYMHC4MGDbLYpqKv1bPPPivExMQImzdvFv744w9h8ODBwpAhQwS9Xm9VTCx4akDRL5uSZsyYIbRr104wGo2CIAhCv379hFdeeUWyzaOPPiqMGTOmRmKsat9++60QEhIipKamCoJgfsM++uijkm1eeeUVYcCAAbYIr9ISEhKEyMhI4euvv7YoeOz5NSwqeMp6rxax5/zOnTsnhIWFCX/99Ve52zjKe7SkXr16CWPHjhUf2/NrOHPmTKFXr16CyWQS2/bv3y+EhIQI//zzj9hmrzk+++yzwoMPPihpW7NmjRAeHi6kp6dLtrOH92nRZ5sgCMLUqVPLLHgq8lr9+++/QkhIiPD333+LbefOnRO0Wq2wefNmq2LilFYN8Pb2tmgLDQ1FTk4O8vLykJKSguTkZAwYMECyzcCBA7F//37odLqaCrXKeHl5AQD0ej10Oh1iY2Nx3333SbYZOHAgzp07h4sXL9ogwsqZO3cuRowYgcDAQEm7I76GJdl7fj/++CMCAgLQo0ePMvsd6T1a5N9//8XFixdx//33A7D/19BgMMDNzQ0ymUxs8/DwAABxeYA95xgfH4+uXbtK2rp16wa9Xo89e/YAsK/3qVx++/Kioq/V7t27odFoJN+boKAghIaGYvfu3dbFZNXWVGUOHz4Mf39/uLu7IzExEQAsPkSDg4Oh1+vLnKOujYxGIwoLCxEXF4fly5ejV69eCAgIwIULF6DX6xEUFCTZPjg4GADE/Gu7rVu34syZM3jxxRct+hzlNRw8eDBCQ0PRu3dvfPLJJ+L6AXvP7+jRowgJCcFHH32ELl26oHXr1hgxYgSOHj0KAA7zHi1p06ZNcHV1Re/evQHY/2v40EMP4dy5c/jyyy+RnZ2NlJQUfPDBBwgLC0O7du0A2HeOhYWFUKlUkraix+fOnQPgWO/Tir5WiYmJCAwMlBS6gLnosTZfp7uIlyrp0KFD2LJlC6ZOnQoAyMzMBABoNBrJdkWPi/pru549eyItLQ0A0L17d7z//vsAHCO//Px8LFiwAJMnT4a7u7tFv73n6Ovri4kTJ6Jt27aQyWTYsWMHFi9ejLS0NMyaNcvu80tPT8eJEydw5swZvPnmm3BxccHHH3+MZ599Ftu2bbP7/EozGAz47bff0KtXL7i6ugKw//dohw4dsGzZMvzvf//DW2+9BcA8Ur569WooFAoA9p1js2bNcOzYMUnbf//9B6A4bnvOr7SK5pKVlSWO5JXk6emJEydOWPWcLHhqWGpqKiZPnozo6GiMGjXK1uFUqZUrVyI/Px8JCQlYsWIFxo8fj88++8zWYVWJFStWwMfHBw8//LCtQ6kW3bt3R/fu3cXH3bp1g1qtxtq1azF+/HgbRlY1BEFAXl4ePvzwQ7Rq1QoA0LZtW/Tq1Qvr169Ht27dbBxh1dq7dy8yMjLKPTPGHv3777947bXX8Mgjj+Dee+/FzZs38dFHH+G5557DV199BWdnZ1uHeFcef/xxvPHGG1i7di2GDh2KhIQELF68WCzm6O5xSqsGZWVlYezYsfDy8sLSpUvFOU5PT08AsDiNOysrS9Jf27Vq1QpRUVEYPnw4PvroI8TGxmL79u12n9+lS5fw6aef4qWXXkJ2djaysrKQl5cHAMjLy0Nubq7d51iWAQMGwGg0Ij4+3u7z02g08PLyEosdwLzOLCwsDAkJCXafX2mbNm2Cl5eXpJCz9xznzp2Lzp07Y9q0aejcuTPuu+8+rFy5EidPnsTGjRsB2HeODz30EJ566iksXLgQ0dHRePrppzFixAh4enrCz88PgH3nV1pFc9FoNMjJybHYPzMz0+p8WfDUkIKCAowbNw7Z2dlYvXq1ZIiuaD629HxkYmIilEolmjRpUqOxVgWtVgulUokLFy6gadOmUCqVZeYHwGI+ura5ePEi9Ho9nnvuOXTs2BEdO3YURz1GjRqFZ555xiFfw5LsPb8WLVqU21dYWGj379GSCgoK8Mcff+C+++6DUqkU2+39NTx37pykYAWABg0aoF69erhw4QIA+85RLpdj+vTpOHDgADZu3Ih9+/bhkUceQUZGBtq2bQsADvU+rehrFRQUhKSkJIvr1iUlJVmdLwueGmAwGDBp0iQkJiZi9erV8Pf3l/Q3adIEzZs3x9atWyXtW7ZsQZcuXSwWstmDo0ePQq/XIyAgACqVCtHR0fj9998l22zZsgXBwcEICAiwUZQVExoainXr1kn+vf766wCAOXPm4M0333TI13DLli1QKBQICwuz+/x69uyJmzdvIj4+Xmy7ceMG4uLiEB4ebvfv0ZJ27NiBvLw88eysIvb+GjZq1AgnT56UtF26dAk3btxA48aNAdh/joD5zLNWrVpBo9Hgiy++QEBAAO655x4AcKj3aUVfq5iYGGRmZmL//v3iNklJSTh58iRiYmKsek6u4akBc+bMwc6dOzFt2jTk5OSIC9EAICwsDCqVChMnTsSUKVPQtGlTREdHY8uWLTh27BjWr19vu8AraMKECWjdujW0Wi2cnZ1x6tQprFmzBlqtFn369AEAPP/88xg1ahRmz56NAQMGIDY2Fps2bcKiRYtsHP2daTQaREdHl9kXHh6O8PBwALDr13D06NGIjo6GVqsFYL6i7XfffYdRo0bB19cXgH3n16dPH0REROCll17C5MmToVarsXLlSqhUKjz++OMA7Ps9WtKvv/6KRo0aoX379hZ99vwajhgxAvPmzcPcuXPRq1cv3Lx5U1xbV/LUZnvN8dixYzh48CBCQ0NRUFCAHTt2YOPGjVi1apVkHY+9vE/z8/Oxa9cuAObCNCcnRyxuOnXqBG9v7wq9VkVX1Z4+fTqmTp0KtVqNRYsWQavVol+/ftYFZdVVe6hSevbsKYSEhJT5LyUlRdzuu+++E/r27SuEh4cLgwcPFnbs2GHDqCvuk08+EYYOHSpERUUJkZGRwqBBg4TFixcL2dnZku2KrpAZHh4u9O3bV9iwYYONIr57Bw4csLjwoCDY72v49ttvC/369RPatGkjtG7dWhg8eLCwdu1ayUXeBMF+8xME8wVAp0yZIrRv315o06aN8Oyzzwpnz56VbGPv79GbN28K4eHhwsKFC8vdxl5fQ5PJJHz11VfC/fffL0RGRgpdu3YVXnzxRSEhIcFiW3vM8eTJk8Lw4cOFyMhIITIyUnjqqaeEf//9t8xt7eF9mpKSUu7n3oEDB8TtKvJaZWVlCa+//rrQoUMHITIyUpgwYYJ4UVtryASh1MQYERERkYPhGh4iIiJyeCx4iIiIyOGx4CEiIiKHx4KHiIiIHB4LHiIiInJ4LHiIiIjI4bHgISIiIofHgoeIiIgcHgseIiIicngseIiIiMjhseAhIiIih/f/txlEBN481r0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "consus = [\n",
    "    [17.458678034799437,1.4912730859858647,2.1801473604781285],\n",
    "    [62.77888315383876,4.153386402343001, 2.2382326860513007],\n",
    "    [253.3399740340455,34.50407603276628,6.778022644775255],\n",
    "    [589.3519345812853,68.59302657452368,6.744186418397083]\n",
    "]\n",
    "data_value = np.asarray(consus)\n",
    "batch_list = [16, 32, 64, 96]\n",
    "batch = np.asarray(batch_list)\n",
    "data = pd.DataFrame(data_value, batch, columns=[\"hbbft\", \"dumbo\", \"hbbft_shard\"])\n",
    "\n",
    "\n",
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure3 n=16\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "consus = [\n",
    "    [16.658536370311463,0.9016208222934177,1.6226261769022257],\n",
    "    [16.890742225306376,1.0286402638469423, 1.7832987861973901],\n",
    "    [17.126071870326996,1.1799578581537518,1.9127273900168285],\n",
    "    [17.372268700173922,1.329521309052195,1.9876470203910555],\n",
    "    [17.458678034799437,1.329521309052195,2.1801473604781285]\n",
    "]\n",
    "data_value = np.asarray(consus)\n",
    "# batch_list = [16, 32, 64, 96]\n",
    "# tps_list = [999.344651271388,4776.674911356351,17784.056132237685,30785.888498003704, 40453.59206446519]\n",
    "tps_list = [999.344651271388,13551.868286753004, 20000, 30000, 40453.59206446519]\n",
    "batch = np.asarray(batch_list)\n",
    "data = pd.DataFrame(data_value, tps_list, columns=[\"hbbft\", \"dumbo\", \"hbbft_shard\"])\n",
    "\n",
    "\n",
    "sns.lineplot(data=data, palette=\"tab10\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.13 ('hbb')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7f5c99d094b399fa9e65cc4107643c6d5293cb8e1c21c8f7db515a0780c667bb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
